MODIS MAIAC高分辨率气溶胶光学厚度产品在干旱区的适用性研究

陈香月; 丁建丽; 王敬哲; 葛翔宇; 张子鹏; 张喆; 左洪超

doi:10.11834/jrs.20220508

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MODIS MAIAC高分辨率气溶胶光学厚度产品在干旱区的适用性研究
Validation of the fine resolution of the MODIS MAIAC aerosol optical depth product over arid areas
2023年27卷第2期页码：406-419
收稿：2020-11-12，

纸质出版：2023-02-07
DOI： 10.11834/jrs.20220508
稿件说明：

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陈香月，丁建丽，王敬哲，葛翔宇，张子鹏，张喆，左洪超.2023.MODIS MAIAC高分辨率气溶胶光学厚度产品在干旱区的适用性研究.遥感学报，27（2）： 406-419 DOI： 10.11834/jrs.20220508.

Chen X Y，Ding J L，Wang J Z，Ge X Y，Zhang Z P，Zhang Z and Zuo H C. 2023. Validation of the fine resolution of the MODIS MAIAC aerosol optical depth product over arid areas. National Remote Sensing Bulletin， 27（2）：406-419 DOI： 10.11834/jrs.20220508.

摘要

多角度大气校正MAIAC（Multiangle Implementation of Atmospheric Correction）是一种新的获取高分辨率气溶胶光学厚度AOD（Aerosol Optical Depth）的通用算法，其空间分辨率高达1 km，在改善陆地暗表面和亮表面AOD反演方面具有很大潜力，但在资料稀缺的干旱区的适用性尚不可知。基于此，利用2000年—2019年AERONET气溶胶站点观测数据，对干旱背景下的MAIAC AOD开展适用性评价。结果表明：大量的MAIAC和AERONET AOD匹配显示了MAIAC AOD在不同时间尺度和下垫面类型都具有较好的信息表征能力。MAIAC AOD在月、季和年尺度上都具有良好的应用前景，总体效果呈现出较好的适用性（

= 0.821，RMSE = 0.117），但存在轻微的低估现象。MAIAC AOD在4种典型地表都具有良好的应用优势，其中草地有效匹配数据最多并呈现高估现象，建筑用地受气溶胶模型影响最大，农田受地表反射率干扰强烈且低估最明显（Below EE = 46.5%）。MAIAC AOD双星观测Terra和Aqua准确性较为一致，

都达到了0.75以上，同时也都存在低估现象，其中Aqua的低估效果更明显。总体而言，MAIAC AOD在干旱背景下适用性良好，具有研究干旱区精细气溶胶特性的潜力，并可进一步促进干旱区大气污染研究。

Abstract

The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km). It is expected to have good potential in improving the AOD inversion of dark and bright surfaces of land. The high spatial resolution of the MAIAC retrievals enhances the capability to distinguish aerosol sources and determine subtle aerosol features. Retrieval of satellite aerosol properties is therefore often challenging due to considerable seasonal variations in surface reflectance and aerosol properties. To date

MAIAC AOD over arid regions under data-scarce environments has been evaluated. Considering these uncertainties

a systematic effort was made to evaluate the MAIAC AOD over arid areas using Aerosol Robotic Network (AERONET) ground-based AOD from 2000 to 2019. Considerable MAIAC-AERONET AOD matchups demonstrate the capability of MAIAC to retrieve AOD over varied ground surfaces and temporal scales. We employed a broader perspective and evaluated MAIAC performance under varying aerosol loading

aerosol types

surface coverage

and viewing geometry. The results show that (1) MAIAC performed well over various temporal scales

including monthly

seasonal and annual scales. Although underestimation is prevalent

MAIAC AOD in the spring and winter months correspond to the highest and lowest retrieval accuracies

respectively. (2) MAIAC performed well over four different typical land surface land surfaces

showing the highest retrieval accuracy over grassland

yet it slightly overestimated AOD. Construction land is most affected by the aerosol model

and farmland is strongly disturbed by surface reflectance and underestimated most obviously (Below EE = 46.5%). (3) The accuracies of the MAIAC AOD observations of Terra and Aqua are similar;

is more than 0.75

but both are underestimated

especially for Aqua. In general

MAIAC’s ability to provide AOD at high spatial resolution appears promising over arid areas and is expected to be helpful to study the characteristics of fine aerosols in arid areas and promote the study of local air quality.

关键词

Keywords

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